Spatiotemporal pattern recognition for neurological event detection and prediction in an implantable device

• US 20040054297A1
• Filed: 09/13/2002
• Published: 03/18/2004
• Est. Priority Date: 09/13/2002
• Status: Active Grant

• Current Assignee: Neuropace Inc.
• Original Assignee: Neuropace Inc.
• Inventors: Tcheng, Thomas K., Wingeier, Brett M.
• Granted Patent: US 7,277,748 B2
• Time in Patent Office: Days
• US Class Current: 600/544
• CPC Class Codes: A61B 5/0031 Implanted circuitry
• CPC Class Codes: A61B 5/076 Permanent implantations telemetry using implanted circuitry A61B5/0031; implanted stimulators for therapy A61N1/3605, A61N1/362, A61N1/372
• CPC Class Codes: A61B 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/374 Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
• CPC Class Codes: A61B 5/377 using evoked responses
• CPC Class Codes: A61B 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy
• CPC Class Codes: A61B 5/7257 using Fourier transforms

Abstract

A system and method for detecting and predicting neurological events with an implantable device uses a relatively low-power central processing unit in connection with signal processing circuitry to identify features (including half waves) and calculate window-based characteristics (including line lengths and areas under the curve of the waveform) in one or more electrographic signals received from a patient'"'"'s brain. The features and window-based characteristics are employed within the framework of a programmable finite state machine to identify patterns and sequences in and across the electrographic signals, facilitating early and reliable detection and prediction of complex spatiotemporal neurological events in real time, and enabling responsive action by the implantable device.

Claims

1. A method for responding to a neurological event identified by analyzing an electrical signal from a patient'"'"'s brain with an implantable device, the method comprising the steps of:

receiving the electrical signal with an electrode pair;

processing the electrical signal with a detection subsystem;

identifying a neurological event in the electrical signal with the detection subsystem, wherein the neurological event is defined by a sequence;

generating an indication of the identified neurological event; and

causing the implantable device to perform an action in accordance with the indication.

2. The method for detecting a neurological event of claim 1, wherein the sequence comprises a plurality of waveform segments.

3. The method for detecting a neurological event of claim 2, wherein the plurality of waveform segments includes a first segment and a second segment.

4. The method for detecting a neurological event of claim 3, wherein the first segment is observed at a first time in a first location in the patient'"'"'s brain.

5. The method for detecting a neurological event of claim 4, wherein the second segment is observed at a second time in a second location in the patient'"'"'s brain.

6. The method for detecting a neurological event of claim 5, wherein the second time follows the first time.

7. The method for detecting a neurological event of claim 5, wherein the first location is substantially the same as the second location.

8. The method for detecting a neurological event of claim 5, wherein the first location is substantially separate from the second location.

9. The method for detecting a neurological event of claim 1, wherein the sequence comprises a sequence of signal characteristics.

10. The method for detecting a neurological event of claim 9, wherein the sequence of signal characteristics is encoded in a finite state machine.

11. The method for detecting a neurological event of claim 10, wherein the finite state machine comprises a master state machine and at least one instance.

12. The method for detecting a neurological event of claim 11, wherein the at least one instance comprises a current state variable and an overall life remaining variable.

13. The method for detecting a neurological event of claim 12, wherein the at least one instance further comprises a state life remaining variable.

14. The method for detecting a neurological event of claim 11, wherein the identifying step comprises the steps of:

setting an instance of the finite state machine to a first state;

detecting a waveform characteristic in the sequence of signal characteristics;

transitioning the instance to a destination state.

15. The method for detecting a neurological event of claim 14, wherein the instance includes a current state, and wherein the transitioning step comprises updating the current state from the first state to the destination state.

16. The method for detecting a neurological event of claim 14, wherein the instance includes a current state, and wherein the transitioning step comprises the steps of:

determining whether a transition associated with the transitioning step is exclusive or nonexclusive;

if the transition is exclusive, updating the current state from the first state to the destination state; and

if the transition is nonexclusive, spawning a new instance in the destination state.

17. The method for detecting a neurological event of claim 10, wherein the identifying step comprises the steps of:

setting the finite state machine to an initial state;

detecting a first waveform characteristic in the sequence of signal characteristics;

transitioning the finite state machine to a first state;

detecting a second waveform characteristic in the sequence of signal characteristics;

transitioning the finite state machine to a detection state; and

performing an action defined in connection with the detection state.

18. An implantable device for detecting a neurological event by analyzing an electrical signal from a patient'"'"'s brain, the device comprising:

an implantable control module;

a central processing unit located within the control module; and

a detection subsystem located within the control module, wherein the detection subsystem is adapted to identify a sequence of characteristics in the electrical signal.

19. The implantable device of claim 18, wherein the detection subsystem performs a first portion of a signal event detection procedure, and the central processing unit performs a second portion of the signal event detection procedure.

20. The implantable device of claim 18, wherein the central processing unit and the detection subsystem are enclosed within a housing, and wherein the housing is implanted in the patient.

21. The implantable device of claim 20, wherein the housing is implanted intracranially in the patient.

22. The implantable device of claim 18, wherein the neurological event comprises an epileptic seizure, an onset of an epileptic seizure, a precursor to an epileptic seizure, an episode of a movement disorder, an onset of an episode of a movement disorder, a precursor to an episode of a movement disorder, a migraine headache, an onset of a migraine headache, a precursor to a migraine headache, an episode of pain, an onset of an episode of pain, a precursor to an episode of pain, an episode of depression, an onset of an episode of depression, or a precursor to an episode of depression.

23. The implantable device of claim 18, wherein the neurological event comprises a signal event predictive of an episode of clinical symptoms selected from the group consisting of an epileptic seizure, an episode of a movement disorder, a migraine headache, an episode of pain, and an episode of depression.

24. The implantable device of claim 18, wherein the detection subsystem comprises at least one electronics module.

25. The implantable device of claim 18, wherein the detection subsystem encodes a finite state machine.

26. The implantable device of claim 25, wherein the finite state machine represents the sequence of characteristics.

27. The implantable device of claim 26, wherein the finite state machine comprises a master state machine and at least one instance.